Server Quality Checklist

Tool Scores

• update_feature_statusA
  3.7/5.0
  Behavior2/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  No annotations are provided, so the description carries full burden for behavioral transparency. It discloses that status updates follow a workflow path, but it does not mention side effects, permissions, validation rules, or what response the agent should expect (e.g., success/failure structure). The phrase 'status or priority' also understates that both can be updated simultaneously per the schema, leaving ambiguity about transactional behavior.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is two sentences, front-loaded with the action verb and resource. It includes the most relevant usage example without wasteful elaboration. Every sentence contributes value.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness3/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  The tool has 5 parameters (1 required, 2 enums) and no output schema. The description covers the core purpose and usage context, but it does not explain return values, behavior on partial updates, or project_id requirements beyond what the schema already states. It is adequate for an agent to invoke, but misses additional behavioral context like response handling.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema coverage is 100%, so the baseline is 3. The description adds minimal meaning beyond the schema: it mentions 'move requests through the workflow' and 'set/change priority,' which mirrors the schema's status and priority enums. It does not clarify the interplay between status and priority or the purpose of status_message, but the schema descriptions already cover each parameter adequately.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states 'Update the status or priority of a feature request' with a specific verb and resource. It distinguishes itself from sibling tools (all read/analysis tools) by being the sole mutation tool, and the workflow example (pending → approved → in_progress → completed) reinforces its purpose.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description explicitly says 'Use this to move requests through the workflow' and 'or to set/change priority,' giving clear context on when to apply the tool. It does not explicitly name alternatives or exclusions, but the sibling list (list_*, search_*, analyze_*) makes it obvious this is the only update tool, providing implicit differentiation.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• list_feature_requestsA
  3.8/5.0
  Behavior3/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  No annotations are provided, so the description carries the transparency burden. It mentions return contents (MRR data, vote breakdown) but does not explicitly state read-only behavior, auth requirements, or pagination behavior. There is also a slight inconsistency: description mentions 'full-text search' while the schema says 'filter by title'.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is two sentences, front-loaded with the main purpose, and each sentence adds meaningful information. There is no fluff or redundancy.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness4/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the 7 parameters, high schema coverage, and no output schema, the description adequately conveys the tool's purpose and key output highlights (MRR, votes). It doesn't explicitly address when to choose this over search_feedback, but it provides enough context for typical use.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema coverage is 100%, so each parameter is already described. The description adds context about MRR and vote breakdown but doesn't provide additional detail on parameter formatting or behavior beyond the schema. The 'q' parameter description is somewhat ambiguous against 'full-text search'.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool lists feature requests from FeaturePulse, which is a specific verb and resource. It lists key capabilities (filtering, full-text search, sorting) but does not explicitly differentiate from sibling tools like search_feedback.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides clear context by stating the tool helps prioritize development by business impact, implying it should be used for sorting and reviewing feature requests. It does not explicitly mention alternatives or when not to use it, but the context is sufficient.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• analyze_feedback_by_groupA
  4/5.0
  Behavior3/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  With no annotations, the description carries the full burden. It discloses the read-only nature implicitly via 'Analyze and group' and specifies return values, but it does not explicitly state non-destructiveness, permissions, pagination, or edge cases like empty groups. This is moderate transparency, not minimal.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is two sentences, front-loaded with the action and outputs, and includes relevant examples. Every part contributes to understanding the tool without unnecessary detail.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness4/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  The tool is relatively simple with two params and no output schema. The description covers the return shape ('counts, total votes, and aggregated MRR') and use cases. Minor gaps exist around pagination and exact aggregation behavior, but the essential context is present.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema coverage is 100% with good descriptions for both parameters (group_by enum and project_id with usage note). The description reinforces group_by but adds little beyond the schema. Baseline 3 is appropriate when the schema handles the meaning well.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description uses a specific verb ('Analyze and group'), a clear resource ('all feature requests'), a defined dimension ('status or priority'), and states the outputs ('counts, total votes, and aggregated MRR'). This distinguishes it from siblings like list_feature_requests (which lists) and get_project_stats (which provides stats without grouping).

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides clear usage context with examples ('Ideal for generating summaries like...') and the schema adds guidance for project_id (use list_projects when multiple projects). It does not explicitly state when not to use this tool or name alternatives, but the context is strong.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• get_project_statsA
  4/5.0
  Behavior3/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  No annotations are provided, so the description carries the full burden of behavioral disclosure. It clearly states this is a read-only overview ('Get', 'statistical overview') and lists the output contents. However, it does not explicitly disclose potential limitations like performance implications, data freshness, or that no permanent changes are made. While read-only is self-evident from the verb, a more explicit note about side effects or permissions would strengthen transparency.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is two sentences long, front-loaded with the tool's purpose and key deliverables, then closes with a guidance sentence. Every phrase earns its place—no filler, no repetition of schema content, and no extraneous details.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness4/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool is a simple statistical overview with one optional parameter and no output schema, the description adequately communicates both input and expected output. It covers the high-level contents and even provides a usage sequence relative to sibling tools. The only minor gap is not mentioning error scenarios or the exact format of the top-10 lists, but these are not critical for a tool of this simplicity.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  The schema provides 100% coverage for the only parameter (project_id) with a useful description including optionality and cross-reference to list_projects. The tool description itself adds no further parameter detail, but since the schema already fully explains the parameter, the baseline of 3 is appropriate.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description opens with a specific verb ('Get') and a concrete resource ('high-level statistical overview of your FeaturePulse project'). It enumerates exactly what is included (total requests, votes, MRR grouped by status/priority, top-10 lists), making the tool's function unambiguous and clearly distinct from the sibling tools which operate on individual items.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description explicitly states when to use the tool: 'Use this before diving into individual requests to understand the overall landscape.' This gives clear contextual guidance. It does not explicitly name alternatives or exclusions, but the sibling list and the phrase 'before diving into individual requests' imply a contrast with detailed-level tools like list_feature_requests, which is sufficient.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• search_feedbackA
  4/5.0
  Behavior3/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  With no annotations, the description carries the burden of disclosing behavior. It states that results are 'most relevant matching requests' and includes vote counts and MRR, implying a read-only search with relevance ranking. However, it does not mention potential side effects, auth requirements, or behavior such as pagination or default project scoping, which would increase transparency.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is two concise sentences, front-loaded with the core action and followed by the key output and use cases. Every word adds value, with no redundancy or fluff.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness4/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given there is no output schema, the description adequately covers the return content ('vote counts and MRR') and the use case. It does not describe the response structure in detail, but for a search tool with three well-documented parameters, this is sufficient context for an agent to select and invoke the tool.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema coverage is 100%, so the baseline is 3. The description does not add new meaning beyond what the schema already provides for parameters like 'q' and 'limit.' It does not explain parameter interactions or edge cases, but the schema itself is descriptive, so no major gap exists.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's function: 'Search feature requests by a text query.' It specifies the resource (feature requests) and the action (search), and adds detail about returning 'vote counts and MRR,' which distinguishes it from sibling tools like list_feature_requests that list all requests rather than searching.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides clear context for when to use the tool: 'useful for finding related feedback before opening a new request or exploring a specific feature area.' It implies when to use it but does not explicitly exclude alternatives or mention when not to use it, hence a 4 rather than 5.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• list_projectsA
  4.2/5.0
  Behavior3/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  With no annotations, the description states a non-mutating action ('List') and notes API key access, but does not disclose additional behaviors like pagination, error handling, or return structure beyond implying project IDs.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  Two sentences, front-loaded with the core action, no redundant information.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness4/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  For a simple list tool with no parameters, the description gives a clear purpose and usage context. It doesn't enumerate return fields but implies project_id, which is sufficient for the stated use case.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters4/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  There are zero parameters, so the baseline is 4. The description adds value by explaining that the output provides project_id for other tools.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description uses the verb 'List' with resource 'projects' and qualifies it with 'accessible with your API key', distinguishing it from sibling tools like list_feature_requests and get_project_stats.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  It explicitly advises to use this tool to find the project_id needed for other tools when multiple projects exist, providing clear context. It doesn't name alternatives but the purpose relative to other tools is evident.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.